It is interesting to compare DHTs and virtual queues at an abstract level. A DHT implements a virtual hash table using a set of multiple collaborating peers, and there are many approaches to implementing DHTs – the main differences being the mechanisms for requesting routing and object-peer assignment. Similarly, a virtual queue also implements a (doubly-ended) FCFS queue using multiple, distributed peers, and the goal is to meet the key requirements mentioned previously.
FCFS queues or the associated scheduling policies are not a new concept per se and they have been an important research topic in operating systems, parallel computing, networking, and other research fields. However, its use as a fairness measure for resource scheduling in P2P networks is uncommon. This is quite expectable for several reasons. First, to be effective, FCFS policies and other closed related policies such as least-used-first policy (when deciding which item to kick out off the cache) or earliest-starting-time-first heuristics (when scheduling jobs over multiple processors) more or less need global and timely status about the resources to be scheduled. Implementing FCFS policies in P2P networks will most likely incur unnecessary overhead.
Secondly, and probably more importantly, network-wise FCFS fairness is irrelevant in application areas such as file sharing supported by P2P systems, where resources offered by providers (who earn some credits as reward) being shared are expected to last for some time.
Even for P2P networks sharing generic, uniform resources such as machine cycles, as mentioned, the usual goal is to improve job processing rate, in which case imposing FCFS fairness seems to reduce the processing rate, especially when the network grows larger and the request pattern is highly skewed.
When resource scheduling is concerned, it is interesting to compare SETI@Home with traditional systems such as operating systems, clusters, or grids, which often need to predict the performance characteristics of the participating resource providers painstakingly in order to derive a suitable execution plan, yet only to find that the predicted performance model disagrees with actual machine statistics due to machine dynamism. In the SETI@Home architecture, instead, the scheduling is done automatically by the resource providers since their act of registration indicates that they are available for the moment, fully respecting machine dynamism. Our resource sharing model bears the same idea as SETI@Home’s, but generalized it in some aspects. First, unlike in SETI@Home where the central server is the one who keeps the work to be done, ours leaves what to be done to resource requestors. In addition, the central server is replaced with a set of collaborating managers that implements the virtual FCFS queue, hoping to improve locality, load balance, fault tolerance, and ultimately scalability. Despite the fact that our virtual queue may incur unavoidable communication delay due to FCFS requirements.
Interestingly, however, the FCFS fairness can play an important role in designing sound incentive mechanisms. For example, if the resource consumers and resource providers are the same set, and whether a consumer can receive resources it needs only after it has earned corresponding credits by providing matching resources. This scenario is not uncommon, and similar works have been done on file-sharing P2P systems ([1] and [11]) where a peer gets paid for providing a specific file to a remote peer, and earned credits are subsequently used for the peer to request a file at another peer. Clearly, without proper FCFS fairness, such incentive mechanisms cannot guarantee that participants with equal capability and willingness to contribute (and consume) may receive unfair treatment.
Our investigation focuses nevertheless on a narrow scope that can be outlined as follows.
First, in our simplified model, jobs are uniform, that is, they are of the same type and same
processing complexity statistically. Secondly, providers are of the same processing power so that the execution time for a given job is the same when run by different providers. These assumptions are made to avoid some pathological cases. Although it is possible to drop these assumptions, doing so may raise new issues of fairness again, but they are nevertheless interesting questions that can be pursued further.
As an example, what is considered a proper pricing of processing a job? It is natural to associate prices with number of instructions and/or space used rather than by mere job counts.
By distinguishing job counts from job pricing, however, the notion of fairness needs to be re-evaluated. By ensuring that each awaiting provider receives fair treatment in terms of job counts, as demanded by our fairness model addressed above, can some providers eventually earn much more than the others under certain request patterns? The problem becomes more challenging when providers can have quite diverse processing power. Although it is natural to demand a “capitalism-oriented” policy that capable providers should receive requests proportional to their processing capabilities. Again, assume all providers participate eagerly in a P2P network and all other aspects being equal, can some providers eventually earn much more than their peers in a way disproportional to their processing capabilities?
Another aspect that needs further investigation is to ensure FCFS fairness for both resource requestors and providers simultaneously. Certainly, a straightforward centralized implementation can achieve this goal, but more effective scheduling algorithms not only need to minimize additional management overhead, but also to resist as many pathological patterns of requests from both consumers and providers.